Adaptive playouts for online learning of policies during Monte Carlo Tree Search

Graf, Tobias; Platzner, Marco

doi:10.1016/j.tcs.2016.06.029

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…The success of MCTS applications to games with perfect information, such as Chess and Go, motivated the researchers to apply it to games with more complicated rules such as card games, real-time strategy (RTS) and other Algorithm 1: MCTS with adaptive playouts (Graf and Platzner, 2016)…”

Section: Games With Imperfect Informationmentioning

confidence: 99%

Monte Carlo Tree Search: A Review of Recent Modifications and Applications

Świechowski,

Godlewski,

Sawicki

et al. 2021

Preprint

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Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-ofthe-art technique for combinatorial games, however, in more complex games (e.g. those with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problemdependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey has been published in 2012. Contributions that appeared since its release are of particular interest for this review.

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Section: Games With Imperfect Informationmentioning

confidence: 99%

Monte Carlo Tree Search: A Review of Recent Modifications and Applications

Świechowski,

Godlewski,

Sawicki

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…PPA is therefore closely related to reinforcement learning whereas MAST is about statistics on moves. Adaptive sampling techniques related to PPA have also been tried recently for Go with success [23,24].…”

Section: Introductionmentioning

confidence: 99%

Memorizing the Playout Policy

Cazenave

Diemert

2018

Communications in Computer and Information Science

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Monte Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP). Playout Policy Adaptation with move Features (PPAF) is a state of the art MCTS algorithm that learns a playout policy online. We propose a simple modification to PPAF consisting in memorizing the learned policy from one move to the next. We test PPAF with memorization (PPAFM) against PPAF and UCT for Atarigo, Breakthrough, Misere Breakthrough, Domineering, Misere Domineering, Knightthrough, Misere Knightthrough and Nogo.

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Monte Carlo Tree Search: a review of recent modifications and applications

Świechowski¹,

et al. 2022

View full text Add to dashboard Cite

Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.

show abstract

Adaptive playouts for online learning of policies during Monte Carlo Tree Search

Cited by 6 publications

References 17 publications

Monte Carlo Tree Search: A Review of Recent Modifications and Applications

Monte Carlo Tree Search: A Review of Recent Modifications and Applications

Memorizing the Playout Policy

Monte Carlo Tree Search: a review of recent modifications and applications

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